## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----hiddensetup, echo=FALSE, message=FALSE----------------------------------- library(knitr) library(rmarkdown) doctype <- opts_knit$get("rmarkdown.pandoc.to") ## ----setup, echo=TRUE, message = FALSE---------------------------------------- library(microbiomeExplorer) ## ----eval=FALSE,echo=TRUE----------------------------------------------------- # runMicrobiomeExplorer() ## ----eval=TRUE, echo=TRUE----------------------------------------------------- data("mouseData", package = "metagenomeSeq") meData <- filterMEData(mouseData,minpresence = 1, minfeats = 2, minreads = 2) ## ----eval=TRUE, echo=TRUE, warning=FALSE-------------------------------------- makeQCPlot(meData, col_by = "diet", log = "none", filter_feat = 101, filter_read = 511, allowWebGL = FALSE) plotlySampleBarplot(meData, col_by = "diet") ## ----eval=TRUE, echo=TRUE----------------------------------------------------- meData <- filterMEData(mouseData,minpresence = 1, minfeats = 100, minreads = 500) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- meData <- normalizeData(meData,norm_method = "Proportion") ## ----eval=TRUE, echo=TRUE----------------------------------------------------- new_pheno <- interaction(pData(meData)[,c("mouseID","relativeTime")]) mutatedRows <- row.names(pData(meData)) mutatedData <- dplyr::mutate(pData(meData), "mouse_time" = new_pheno) row.names(mutatedData) <- mutatedRows meData <- addPhenoData(meData,mutatedData) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- bufcolnames <- names(fData(meData)) df <- as.data.frame(t(apply(fData(meData),1, rollDownFeatures))) names(df) <- bufcolnames meData <- addFeatData(meData,df) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- aggDat <- aggFeatures(meData, level = "genus") ## ----eval=TRUE, echo=TRUE, warning=FALSE-------------------------------------- plotAbundance(aggDat, level = "genus", x_var = "diet", facet1 = NULL, facet2 = NULL, ind = 1:10, plotTitle = "Top 10 feature percentage at genus level", ylab = "Percentage") ## ----eval=TRUE, echo=TRUE----------------------------------------------------- plotSingleFeature(aggDat, x_var = "diet", ind = 1:10, plotTitle = "Percentage of Enterococcus", facet1 = NULL, facet2 = NULL, feature = "Enterococcus", ylab = "Percentage", log = TRUE, showPoints = TRUE) ## ----eval=TRUE, echo=TRUE----------------------------------------------------- plotAlpha(aggDat, level = "genus", index = "shannon", x_var = "diet", facet1 = NULL, facet2 = NULL, col_by = "mouseID", plotTitle = "Shannon diversity index at genus level") ## ----eval=TRUE, echo=TRUE, message = FALSE, warning = FALSE------------------- distMat <- computeDistMat(aggDat, "bray") pcaVals <- calculatePCAs(distMat, c("PC1", "PC2")) plotBeta(aggDat, dist_method = "bray", pcas = pcaVals, dim = c("PC1", "PC2"), col_by = "diet", shape_by = NULL, plotTitle = "Bray-Curtis diversity at genus level", pt_size = "6", plotText = "R2: 0.478; Pr(>F): 0.002", confInterval = 0.95, allowWebGL = FALSE) ## ----eval=TRUE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height = 10---- plotHeatmap(aggDat, features = NULL, log = TRUE, sort_by = "Variance", nfeat = 50, col_by = c("diet"), row_by = "", plotTitle = "Top 50 features sorted by Variance at genus level") ## ----eval=TRUE, echo=TRUE, warning = FALSE, message = FALSE------------------- cf <- corrFeature(aggDat, feat1 = "Bacteroides", feat2 = "Prevotella", log = TRUE, facet1 = "diet", facet2 = NULL, method = "spearman", plotTitle = "Spearman correlation of Bacteroides vs Prevotella split by diet", col_by = "status", allowWebGL = FALSE) ## ----eval=TRUE, echo=TRUE, warning = FALSE, message = FALSE------------------- diffResults <- runDiffTest(aggDat, level = "genus", phenotype = "diet", phenolevels = c("BK", "Western"), method = "DESeq2") kable(head(diffResults)) ## ----eval=TRUE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height = 10---- plotLongFeature(aggDat, x_var = "date", id_var = "mouseID", plotTitle = "Abundance of Prevotella", feature = "Prevotella", ylab = "Reads", log = TRUE, x_levels = c("2007-12-11","2008-01-21","2008-02-11","2008-02-25"))